1.1. Technical Field
This invention relates to computer-based computational methods and systems to perform empirical induction. Empirical induction involves procedures to arrive at generalized conclusions and to make predictions from data. In particular, this document addresses procedures for using repeated measures data to quantify, discover, analyze, and describe longitudinal associations between events and variables for individuals.
1.2. Description of Related Art
Statistical analysis is the prevailing computational method to perform empirical induction. Empirical induction is used to gain scientific knowledge, to develop and evaluate treatments and other interventions, and to help make predictions and decisions. This document focuses on empirical induction about patterns of association between and among variables.
Computational methods and systems of empirical induction are designed to provide high quality generalized conclusions and predictions. Generalized conclusions and predictions based on generalized conclusions are considered to be of high quality when they meet four criteria. First, the generalized conclusions and predictions are of high quality when they are based on observation and experience that is recorded as data that can be shared. Second, the generalized conclusions and predictions are of high quality when the data are properly analyzed by computational procedures that can be specified in detailed protocols, the protocols making the procedures transparent. Third, the generalized conclusions and predictions are of high quality when application of the protocols to the data yield results that can be reliably repeated by the same investigator and reproduced by other investigators. Fourth, the generalized conclusions and predictions are of high quality when they are not apt to be falsifiable by new or additional data.
The Appendix is an outline that helps reveal the logical structure of this document. Section 2.9 defines many terms used in this document.
1.2.1. Fundamental Limitations of the Statistical Method and a Derivative Nexus of Problems and Needs
This section identifies four fundamental limitations of the statistical method and illustrates a common set of conditions under which these limitations lead to a nexus of related problems and needs. This section also offers a prime example of how the nexus of problems and needs hinders progress in science, some professions, and the advancement of human welfare.
There are two major research strategies for investigating individuals. First, individuals can be investigated directly as individuals. Second, individuals can be investigated indirectly as members of groups or collective entities. The statistical method primarily is a component of the second research strategy. The statistical method includes descriptive statistics for describing groups and populations as well as inferential statistics. Inferential statistics uses statistical descriptions of statistical samples to make inferences about populations.
The first fundamental limitation is that the statistical method is not well suited to perform empirical induction for individuals. In other words, the statistical method often is not well suited to provide high quality generalized conclusions about and predictions for individuals. For example, the value of a statistical measure such as a group mean may not describe any individual member of the group.
It is possible for applications of both the direct and the indirect research strategies for investigating individuals to arrive at similar high quality generalized conclusions and predictions. However, conditions suitable for the achievement of similar high quality generalized conclusions and predictions with the two different research strategies often do not obtain.
Conditions not favorable for similar high quality generalized conclusions and predictions with the direct and indirect research strategies for investigating individuals can be illustrated in the context of medicine. Investigations of phenomena in which individuals could be investigated either directly as individuals or indirectly as members of groups could be expected to arrive at similar high quality generalized conclusions about individuals if individual patients were clones with identical histories. Problems arise in clinical research and medicine because patients are not clones with identical histories. In areas of investigation such as medicine, it is perfectly possible for applications of the statistical method to arrive at high quality generalized conclusions about groups and high quality inferences about populations but low quality predictions for individual members of the groups or populations. For example, individual patients may not respond to a treatment in the same way that most patients in a group respond to the treatment.
The first fundamental limitation of the statistical method has two parts. The first part of the first limitation is that the statistical method is not well suited to be applied during investigations of unique individuals. Individuals can be unique either because they are so particular or unique because they are so inclusive. Individual patients with particular genomes and histories are unique because they are so particular. The world economy, the worldwide investment market, and the worldwide health-related environmental system are each unique because each is so inclusive.
The second part of the first fundamental limitation of the statistical method is that the statistical method is not well suited to reveal that which may make individual group members different with respect to associations between and among variables. Without recognizing that which may make individual group members different, it is difficult to develop the classification systems that help make the statistical method useful. The classification systems at issue are, for example, classifications of medical disorders that can be applied to form more homogeneous groups of individuals for investigations and to predict responses of individual patients to treatments.
The second fundamental limitation is that the statistical method is not well suited to arrive at high quality generalized conclusions about longitudinal associations or to make high quality predictions about longitudinal associations. Longitudinal associations are quantified within individuals. The quantification of longitudinal associations would help enable investigations of dynamic functioning including the internal and external control of individuals.
The statistical method is well suited to arrive at high quality generalized conclusions about cross-sectional associations. Cross-sectional associations are quantified across individuals for particular variables effectively at particular times. But generalizations about associations do not have to be generalizations across individuals for particular variables to be generalizations. Generalizations about associations can be generalizations across variables and over time for particular individuals. For example, it is a generalization for an individual to conclude that her allergy symptoms generally get worse after she pets a cat and rubs her eyes.
Biotechnology is making rapid progress in identifying that which makes individuals different in terms of genetic characteristics that are relatively stable over time. It could also be valuable to identify that which makes individuals different in terms of dynamic functioning, functioning that can involve longitudinal associations between the products of genetic expression that fluctuate in level over time.
The limitation of the statistical method for quantifying longitudinal associations, together with the almost exclusive role of the statistical method as a computational method of empirical induction, appears to be the reason why there are so few investigations of longitudinal associations in, for example, the medical literature.
The third fundamental limitation is that the statistical method is not well suited to investigate complexity and multidimensionality. The capability to investigate multidimensionality would allow simultaneous investigations of many variables that can affect generalized conclusions and predictions. The clinical research literature makes references to the curse of multidimensionality. Researchers seeking to satisfy the needs of decision-makers for more detailed information often bemoan situations in which there seem to be more variables that need to be investigated than research subjects to investigate. For areas of research and practice such as medicine, the differences among patients that affect outcomes are more apt to be identified when many variables are examined in detail. Complexity often appears to increase the need to investigate individuals directly as individuals.
Rapidly emerging discoveries are increasing the need for more detailed information about associations and predictors of association. For example, many genetic polymorphisms affect the ways drugs act on bodies and the way bodies act on drugs.
The fourth fundamental limitation of the statistical method can be considered as a corollary of the first three limitations. The statistical method is not well suited for detailed investigations of changing individuals and the emergence of individuals that are unique. Change and emergence often becomes evident in the detailed ways that individuals function, in the ways that individuals are controlled, and in the ways that individuals control themselves.
Aging is one way that individuals change. "Aging" often refers to a variety of changes including changes in dynamic functioning. Although the statistical method is well suited to investigate certain differences between and among groups of individuals of different ages, it is not well suited to investigate change, emergence, and changes of dynamic functioning within individuals.
There are other ways that individuals can change. Individuals develop. Patients may become sensitized or desensitized to the effects of drugs or dependent on the effects of drugs. People may adapt or make the most of disabilities. Animals habituate to stimuli. People learn.
Emergence is change that creates unique entities. Individuals such as people, economies, and investment markets can change to become emergent entities that are unique and function in new ways. As examples, a person may come to function in new ways after a unique lifetime of learning, experience, and thought. People acting on knowledge about predictive associations may change the way economies and investment markets function. Discovery and knowledge of some associations can affect the associations themselves as people act on the knowledge.
In brief, the statistical method is limited as a means to perform empirical induction for individuals. The statistical method is of limited value for investigations of unique individuals and to help reveal that which may make individuals different. The statistical method is limited for investigations of how individuals control themselves and how environments control the dynamic functioning of individuals. The statistical method also is limited for detailed investigations of complexity and of how individuals change in the way they function.
These limitations, together with the prevailing role of the statistical method as a computational and scientific means to perform empirical induction, lead to a nexus of many specific limitations, problems, and needs. The concept of a nexus is used to indicate that the specific limitations, problems, and needs should be considered as a set of related members that can be addressed by a common solution.
This document will identify and address some components of the nexus of problems and needs in the context of applying correlation coefficients and other statistical measures of association to perform empirical induction directly for individuals, which is the first research strategy. This document also will identify and address additional components of the nexus in the context of group clinical trials which investigate individuals indirectly as members of groups, which is the second research strategy.
Here is a prime example of how the nexus of problems and needs hinders progress in science, professions such as medicine that tend to be grounded in science, and the advancement of human welfare.
Conventional clinical research procedures that use the statistical method almost exclusively as a computational method of empirical induction are not well suited to help realize the full potential of biotechnology. Emerging developments related to biotechnology include genotyping and gene expression monitoring, combinatorial chemistry, and rational drug design. Such developments can be viewed as tools for creating biotechnology products that can be targeted more effectively to meet specific medical needs that have been identified for individual patients.
Biotechnology is creating many potential therapeutic products that need to be evaluated. Biotechnology also is setting new milestones for identifying that which makes individuals different and unique in terms of genetic and other characteristics that are relevant and important to treatment decisions. In contrast, clinical research, which currently uses the statistical method almost exclusively as a computational method of empirical induction, works best to investigate that which individual group members have in common. As a result of emphasizing commonality, the statistical method is not well suited to exploit one of the major strengths of biotechnology, namely the capability of biotechnology to provide information about that which makes individuals different or unique.
Conventional clinical research study designs and procedures, which are best suited to investigate that which individual group members have in common, are not effective and efficient for targeting the development and use of biotechnology products to patients who could benefit and away from patients who could be harmed. This problem with conventional clinical research study designs and procedures currently may well be the major correctable factor limiting the achievement of biotechnology's potential to improve human welfare.
Despite its limitations, the statistical method itself is not the problem. The problems herein addressed generally occur for one of two reasons. First, the statistical method is applied to investigations for which it is not best suited, generally for lack of a computational method of empirical induction for investigating individuals directly. Second, the statistical method is applied without also applying a computational method and system specifically designed to provide high quality measures of longitudinal association for individuals as well as for conducting investigations of changing and emergent entities.
1.2.1.1. Limitations of Correlation Coefficients
Correlation coefficients and other conventional measures of association are part of the statistical method. Conventional measures of association are best suited to perform empirical induction for groups. Statistical measures of association were developed primarily to provide quantitative descriptions of cross-sectional associations between variables measured on single occasions for each individual belonging to a group of two or more individuals. Often, the groups are considered to be samples and statistical tests are used to make inferences about associations in populations.
Statistical measures of association are of limited value when there is need to analyze repeated measures data to investigate longitudinal associations for individuals. One limitation is that correlation coefficients can have their maximum absolute value when there are only two repeated measurements of the variables. It is a problem for two measurements to yield the maximum absolute value of 1 because two repeated measurements can provide only a very limited amount of evidence for a longitudinal association.
Correlation coefficients often are tested statistically to investigate evidence for associations. But another limitation is that it is a problem to statistically test correlation coefficients to investigate longitudinal associations using repeated measures data because repeated measurements of particular variables often are themselves correlated. Because such limitations are widely recognized and for lack of a better alternative, longitudinal associations often are investigated by subjective impressions rather than analyzed with statistical measures of association such as correlation coefficients.
The term "subjective impressions" is used in this document to refer to ideas about associations between and among variables that are based on experience but not obtained by applying computational methods of empirical induction to data. People often form subjective impressions about longitudinal associations for individuals.
Here are two examples in which investigators often rely on subjective impressions about longitudinal associations for individuals. These examples support the need for a new computational method of empirical induction. First, clinicians usually rely on subjective impressions about the responses of individual patients to treatments used to manage or control chronic disorders. Current computerized medical records and systems that present patient monitoring data from intensive care often chart the values of multiple treatment and health related measures for individual patients on graphs with a common time axis. But clinicians are left to form subjective impressions about the longitudinal associations that may be investigated to guide treatment decisions and to make prognostic statements.
The second example is that people often form subjective impressions about longitudinal associations involving time-series data for investment markets and economies, individuals that are unique because of their inclusiveness. Current investment charting software often graphs multiple time-series data but investors, advisors, fund managers, and researchers are left to form subjective impressions about longitudinal associations that can be investigated to guide investment decisions and provide knowledge about how economies and investment markets work.
Many problems arise when associations are investigated solely by forming subjective impressions. Subjective impressions seldom are precise. In addition, subjective impressions have limited repeatability by the same investigators and limited reproducibility across investigators forming the subjective impressions.
Subjective impressions can be based either on subjective experience or data. Subjective impressions that are based on data are soft analyses. Soft analyses are carried out with procedures not specified in detailed protocols that specify computational procedures, protocols that can be shared to make the procedures transparent and procedures that can be performed using computers to obtain the same results in a repeatable and reproducible manner.
Many common conditions make it difficult for people to achieve high precision, repeatability, and reproducibility while forming subjective impressions about longitudinal associations. These conditions include the need for impressions to account for episodes of events as well as delays and persistencies in associations between and among variables.
Another common condition that makes it difficult to form precise, repeatable, and reproducible subjective impressions occurs when target outcomes such as health have many components, components that can vary in importance in ways that may be unspecified. For example, internists may consider the blood pressure lowering effects of a drug to be most important, psychiatrists may consider the effects of the same drug on human functioning and mental health to be most important, and patients may use still other importance weights based on their hopes and personal preferences.
It also is difficult to achieve high precision, repeatability, and reproducibility by subjective impressions when many independent variables or predictors have an effect on target events or dependent variables, when the individual predictors vary in predictive power, and when independent variables or predictors interact in various ways.
Additional problems arise from reliance on subjective impressions about longitudinal associations. Preparation to form impressions requires valuable time. The quality of subjective impressions, and those affected by subjective impressions, may suffer when experts are not readily available. Reliance on subjective impressions may limit accountability for services such as medical care. In some cases such as medicine, reliance on subjective impressions may forestall collecting and analyzing data that could contribute to the development of the cumulative systematic experience that is a hallmark of science.
Many problems cited in this and the following sections about clinical trials can be traced to a lack of adequate computational methods and systems to quantify, discover, analyze, and describe longitudinal associations between and among variables. This lack of adequate measures of longitudinal association appears to be a major impediment to progress in many sciences and professions. Here are two examples.
Many complex systems, including living things, regulate themselves internally and adapt to their environments. Yet there do not appear to be any widely accepted methods for measuring internal control and adaptation in many contexts, as internal control becomes evident in the form of longitudinal associations between and among variables measured repeatedly over time for individuals. This lack of adequate measures limits scientific progress, the evaluation of interventions that affect internal control, and the value achieved from measurement and information technologies that are emerging in areas such as health monitoring and serial functional imaging.
The second example involves behavior. Behavior can be conceptualized as a means of system regulation and control that involves associations between stimuli and responses. Learning and conditioning, both forms of behavior modification, can be viewed as changes in these associations between stimuli and responses as individual systems adapt to environmental contingencies. There is need for new options to quantify such associations and contingencies as they become evident over time for individuals.
1.2.1.2. Limitations of Conventional Group Clinical Trial Designs and Procedures
This document uses group clinical trials as an example of a rather highly developed application of the statistical method to an important area of investigation. Clinical trials herein represent the way the statistical method is applied for additional areas of experimental investigation.
The groups referred to by "group clinical trials" are collective entities, classes of two or more individuals. The individuals in the groups or classes are expected to meet certain conditions specified in inclusion and exclusion criteria, criteria that generally make reference to classifications of medical disorders.
Group clinical trials are an example of the second research strategy identified in Section 1.2.1. Individuals are investigated indirectly as members of groups.
Conventional group clinical trials are conducted without also applying the computational method and system for empirical induction that is the object of this document. Statistics in conventional group clinical trials are used to test measures of health. The alternative is to test measures of apparent benefit and harm with respect to health measures. Measures of benefit and harm can be computed by quantifying longitudinal associations between treatment and health for individual patients.
Group clinical trials that are conducted without also applying the present invention are subject to the limitations of the statistical method that were identified in Section 1.2.1. As a result, clinical trials for many treatments are unnecessarily limited in achieving their primary objectives.
The primary objectives of clinical trials are to help develop safe and effective treatments such as drugs and to provide information that can be used to improve care and outcomes for individual patients in clinical practice. Group clinical trials are a good example for presenting this invention because one important function of group clinical trials is to guide decision-making for individual patients.
Conventional clinical trial designs and procedures embody a nexus of many related problems and needs. This nexus hampers achievement of the objectives for many clinical trials. This nexus of problems and needs can be largely overcome for a large and important class of clinical trials, namely trials of many treatments such as drugs that are used to manage or control chronic physical and mental disorders.
Drug treatments to manage or control chronic disorders are distinguished herein from treatments intended to cure. These two classes of treatments are distinguished by a characteristic of best use that helps determine whether or not this invention can be applied to major advantage for the conduct of clinical trials. This characteristic is whether or not the treatments can be made to vary over time for individuals or, similarly, if there are repeated episodes of treatment.
Drugs such as antibiotics that are intended to cure usually are administered in single relatively short episodes for purposes such as eliminating pathogens. Drugs intended to cure, especially after single relatively short episodes of treatment, generally would not be evaluated with this invention. Similarly, the present invention would not be suitable for primary evaluations of surgical procedures.
In contrast, treatments for the management and control of chronic disorders usually are administered over relatively long periods of time to provide ongoing control of signs, symptoms, or pathogens. The present invention offers major advantages for evaluating treatments for the management and control of chronic disorders.
The doses of treatments for the management of chronic disorders often are changed to some degree for gathering information to help determine if treatment should be continued with higher doses, lower doses, or if treatment should be continued at all. Information about how health changes in relation to how treatments change for individual patients can be used to quantify longitudinal associations that indicate treatment effects.
All subsequent sections of this document that address clinical trials address clinical trials in the context of treatments to manage or control chronic disorders.
1.2.1.2.1. The Targeting Problem in Clinical Trials
One major limitation that conventional group clinical trial designs and procedures have in achieving their primary objectives will be referred to as the targeting problem. Targeting consists of identifying the indications and the contraindications for specific treatments. Poor targeting is a problem both for drug development and for clinical practice.
The targeting problem often makes it difficult to target potential treatments to the right patients during drug development. Poor targeting hinders drug development and can prevent marketing approval of drugs that might be approved if the drugs could be targeted more effectively.
Poor targeting in clinical practice means that too many patients receive treatments that are harmful and not enough patients receive the most beneficial treatments. Poor targeting in clinical practice often results because patients' treatments are not individualized or personalized when individual patients are treated as if they were average patients in heterogeneous groups.
Targeting during both treatment development and clinical practice is especially important when patients and disorders are heterogeneous and when treatment options are numerous and diverse. Many chronic disorders and the patients who experience these disorders are unique or different from average patients in ways that affect responses to treatments.
The failure to target more effectively during drug development and clinical practice is costly, both economically in terms of the costs of drug development and ineffective treatment as well as in terms of human welfare.
1.2.1.2.1.1. The Need to Identify Treatment Responders, Placebo Responders, and Predictors of Differential Response
Responders, in the context of clinical trials, are patients for whom changes in health are associated with changes in treatment. Associations may not indicate that treatments under investigation cause the changes in health. In addition, associations can be weak. Some responders may be treatment responders. Treatment responders are patients for whom it is reasonable to conclude that a specific treatment causes a specific response.
Some responders may be placebo responders. Placebo responders are patients who have responded to variables other than the treatments being evaluated. It is important to remember that placebo responders are responders even though the category "placebo responder" generally is used as a wastebasket for patients considered to be problems because they appear to have responded to variables other than the treatments of interest. Investigators seldom specify, control, and account for variables that cause placebo response.
One major factor that contributes to the targeting problem is that conventional group clinical trial designs and procedures do not distinguish treatment responders from placebo responders. Here is an example of this problem that involves drug treatments for clinical depression.
There are many antidepressant drugs that could be expected to have different effects for various classes of patients because the drugs appear to work by several quite different mechanisms of action. Yet it is often noted that many clinical trials that evaluate different antidepressants appear to yield quite similar results. About 1/3 of the patients appear to be placebo responders, about an additional 1/3 appear to be treatment responders, and about the remaining 1/3 do not appear to respond at all. This means that the responders are a heterogeneous group. About 1/2 of the responders are placebo responders and 1/2 of the responders are treatment responders.
The problems created by the failure of conventional group clinical trial designs and procedures to distinguish treatment responders from placebo responders are compounded by other important facts. Health is multidimensional, health is affected by many variables including treatments, and there often is more than one way to bring about a particular health response. It is perfectly possible for a patient to be a treatment responder with respect to one health variable, a placebo responder with respect to another health variable, and to be both a treatment responder and a placebo responder with respect to a particular health variable. As an example of the latter, it is perfectly possible for both a patient's relationship with his psychiatrist and the antidepressant prescribed by the psychiatrist to be therapeutic. The answers to many questions about treatment and placebo response are likely to be treatment, patient, and health variable specific, all at the same time.
Without answering questions about which patients respond to which treatments with which responses, it is difficult to identify predictors of treatment response, predictors of placebo response, and predictors that differentiate treatment response from placebo response. Failures to answer questions about which patients respond to which treatments with which responses are major contributors to the targeting problem in clinical trials.
1.2.1.2.1.2. The Need for Both Detailed and Comprehensive Information
In order to address the targeting problem more effectively, clinical trials need to provide information about treatment effects that is both detailed and comprehensive. The information needs to be detailed in order to match specific effects of different treatments with specific signs, symptoms, and other indications and contra-indications for individual patients.
The information for targeting also needs to be comprehensive because decision-makers pick and choose treatments rather than the effects of treatments. Choosing a treatment for an individual generally means choosing all of its effects for that individual, both beneficial and harmful. More comprehensive treatment evaluations provide information about more of the effects of particular treatments.
Unfortunately, conventional clinical trial designs and procedures make it difficult for treatment evaluations to be both detailed and comprehensive. The more comprehensive evaluations tend to lack detail. Evaluations that provide detail tend to lack comprehensiveness.
Conventional procedures that limit the number of health variables that can be evaluated in particular trials foster controversy about which health variables should be measured or, if measured, which variables should be analyzed as primary variables. The lack of widely accepted comprehensive measures of responses to treatments provides opportunities for those who conduct or sponsor clinical trials to pick and choose health variables in accord with any interests in making treatments look good or bad. In addition, conventional designs and procedures are not well suited to identify treatment effect factors, which are clusters of health variables that are affected similarly by treatments. Identification of such factors could support more rational decisions about how to use scarce resources for measuring health in clinical trials.
The following sections identify four strategies to address the targeting problem as these strategies involve the need for detailed and comprehensive information from treatment evaluations. Each of these strategies has certain problems and limitations. Some strategies raise additional related problems.
1.2.1.2.1.2.1. The Need for Many Analyses and the Problem of Many Tests
The first strategy for achieving treatment evaluations that are both detailed and comprehensive is to perform many statistical tests as part of particular clinical trials. For example, a statistical test may be performed on each of many health variables. This strategy creates problems. The use of many statistical tests in the conduct of particular trials makes it difficult to interpret the statistical significance of any of the tests.
The need for many analyses goes beyond the need to evaluate the effects of treatments on many health variables. In addition, it often would be helpful to evaluate dose-response relationships, delays and persistencies in responses to treatments, episodes of treatments and of responses, as well as Boolean independent events and Boolean dependent events. Such additional analyses can be problematic when they call for many statistical tests.
1.2.1.2.1.2.2. Some Problems with Multivariate Analyses
The second strategy for achieving treatment evaluations that are both detailed and comprehensive is to apply multivariate analyses that evaluate several health variables with one statistical test. One problem is that multivariate analyses, such as analyses based on the multivariate normal distribution, often require that certain assumptions be met in order for the statistical tests to yield valid results. Very often these assumptions are difficult to evaluate and unlikely to be met.
Multivariate analyses appear to have other limitations. They may not be appropriate when many health measures vary in importance and when the measures are used in attempts to achieve more comprehensive evaluations of both efficacy and safety. In addition, multivariate analyses may not be appropriate to evaluate dose-response relationships, delays and persistencies in responses to treatments, episodes of treatments and responses, as well as complex independent and dependent events that can be defined with Boolean operators.
1.2.1.2.1.2.3. The Aggregation Problem with Composite Health Measures
The third strategy for achieving treatment evaluations that are both detailed and comprehensive is to develop composite health measures, which have multiple health components. One use of composite measures is to evaluate treatments for heterogeneous disorders for which diagnostic requirements often include statements of the following form: The patient must experience at least 5 of 8 specific symptoms. Composite health measures include rating scales for disorders such as clinical depression and anxiety.
Current composite health measures for particular indications tend to achieve a degree of comprehensiveness that is limited primarily to efficacy. One problem is that this comprehensiveness is achieved by aggregating information across components before the information is analyzed. Aggregation across components before analysis of treatment effects tends to obscure detail about benefit and harm with respect to the different components and for different patients and subgroups of patients.
1.2.1.2.1.2.3.1. The Weighting Problem for Composite Health Measures
Composite health measures also raise a very important cluster of problems in treatment evaluation that will be called the weighting problem. The weighting problem involves the relative importance of the various effects of treatments. Section 1.2.1.1 includes an illustration of the weighting problem for a blood pressure lowering drug. The weighting problem will be introduced by identifying two primary issues that need to be distinguished in treatment evaluations.
Treatment evaluations generally involve two primary issues. These issues can be addressed by answering two distinct questions. The first question addresses the basic scientific issue: What are the health effects of particular treatments? The second question addresses the applied scientific or valuation issue: How do decision-makers and patients value the various health effects of particular treatments? The valuation issue can be addressed with importance weights, which quantify the relative value of treatment effects with respect to different health variables. Importance weights may vary by person, culture, society, and medical specialty in accord with things such as personal values and preferences, assessments of clinical significance, and social values such as functioning well in social roles including productivity of patients at work.
One problem derives from the fact that conventional clinical trial procedures are not well suited to use explicit importance weights. This lack of usefulness of explicit importance weights with conventional procedures tends to limit research on the best ways to elicit or otherwise determine importance weights, the determination of the importance weights themselves, and the use of importance weights or preference measures that are available.
Very often, investigators use implicit importance weights. Implicit importance weights add a major subjective component to treatment evaluations and appear to be a source of much controversy about the benefit and harm of particular treatments (Section 1.2.1.2.3).
Conventional designs and procedures for treatment evaluation tend to confound the basic scientific issue with the valuation issue. This confounding can occur when implicit importance weights drive the basic scientific investigation by affecting, for example, what health variables are measured or what variables are the objects of primary analyses in clinical trials. One factor that contributes to this confounding is that conventional designs and procedures are limited in their ability to use many dependent or health variables simultaneously to achieve more comprehensive treatment evaluations. Because of this limitation, the selection of health variables may be more restrictive and dependent on importance weights than it may need to be for other reasons such as limited time or resources to measure health. Also, because conventional procedures for simultaneous analyses of repeated measures data for many health variables are limited, many clinical trials use scarce resources to collect much data in attempts to be more comprehensive without extracting much value from these data during analyses.
Another problem is that scoring procedures for composite health measures generally use importance weights that are explicit but fixed. Fixed weights make it difficult to rerun analyses of treatment effects using different importance weights for different decision-makers, individual patients, or groups of patients.
1.2.1.2.1.2.4. Some Problems Involving Hierarchies of Health Measures
The fourth strategy involving comprehensiveness and detail in treatment evaluations concerns hierarchical levels for measuring health and treatment effects. One example of a hierarchy of health measures has the following levels from low to high: laboratory measures, signs and symptoms, mental and physical functioning, general health perceptions, and quality of life.
Conventional clinical trial procedures tend to confound issues concerning comprehensiveness and detail in treatment evaluations with issues concerning levels of measurement of health and treatment effects. This confounding of issues can occur when investigators shift to different levels of health measurement in order to achieve treatment evaluations that are either more detailed or more comprehensive. The alternative to this shifting is to achieve more detail or comprehensiveness at a particular level of measurement. This confounding of issues involving levels of health measurement with degrees of detail and comprehensiveness derives from the notion of a hierarchy itself.
Higher levels in health measurement hierarchies often are thought to encompass and summarize the combined contributions of multiple components at lower levels. For example, general quality of life measures often are considered to provide common metrics that encompass the combined beneficial and harmful effects of diverse treatments with respect to measures at lower levels in the hierarchy such as many signs, symptoms, and laboratory measures. Thus one way to achieve more comprehensive treatment evaluations is to shift to health measures at higher levels in the hierarchy. While often useful for achieving more comprehensive evaluations, this strategy has important limitations. One limitation is that treatment evaluations that are more comprehensive because they use higher levels of health measurement often do not provide the detailed information about specific signs and symptoms that is needed to address the targeting problem (Section 1.2.1.2.1).
Another limitation of using higher-level health measures in medical treatment evaluations is that higher-level measures often are affected by variables outside the domain of medicine. For example, environmental, social, economic, spiritual and personality factors may affect scores obtained with quality of life rating scales. Variability in these factors during the course of treatment can add variability to the measures used in treatment evaluation. This variability appears to make it more difficult to achieve statistical significance with the higher level measures as compared to the lower level health measures.
A related problem is that conventional clinical trial procedures are not well suited to investigate relationships involving health measures at different levels in a hierarchy. Without elucidating relationships among health measures at different levels, it is difficult to determine the extent to which, for example, quality of life is related to more traditional health measures such as laboratory values and ratings of symptom severity.
Some measures are identified as measures of health-related quality of life. To some extent, these measures often require patients to judge how much their quality of life is affected by health as distinct from other conditions that can affect quality of life. Without more adequate procedures for investigating across-level relationships involving more traditional health measures as well as other factors that affect quality of life, it is difficult to determine the accuracy of patient impressions about how health affects their quality of life. The accuracy of these impressions may affect the validity of some health-related quality of life measures.
1.2.1.2.1.2.5. Some Problems Involving the Separation of Safety and Efficacy Evaluations
Conventional clinical trial procedures and regulatory agency guidelines for drug development often separate safety evaluations from efficacy evaluations of particular treatments. This conventional practice appears to be a problem for at least four primary reasons.
The first reason why the separation of safety and efficacy evaluations appears to be a problem is that this practice can impede the development of classification systems for medical disorders. It would appear those medical classification systems intended primarily to guide treatment evaluations and treatment decisions should account for variables predictive of both benefit and harm. This consideration appears to call for more comprehensive medical classification systems. For example, such classifications may need to account for genetic polymorhpisms that affect drug metabolism.
The second reason why separation of safety and efficacy evaluations appears to be a problem is that this practice tends to limit fair and comprehensive treatment evaluations. The reason for this problem is that the conventional practice tends to neglect beneficial effects with respect to health variables considered for safety evaluations. A number of treatments have been developed for new indications, said treatment development projects being initiated by observations of side effects.
The third reason why separation of safety and efficacy evaluations appears to be a problem is that conventional procedures for safety evaluations generally set lower standards for data collection and data analysis than the standards for efficacy evaluations.
Conventional safety evaluation procedures tend to limit the collection of standardized and detailed data about treatment effects. Standardization would be facilitated by systematic elicitation of information about signs, symptoms and other measures that may be affected by treatment. Failure to use systematic elicitation during collection of data on health can introduce variability into treatment evaluation procedures. This variability derives from differences in the personalities, motivations, and diligence of patients and investigators who may or may not provide, elicit, and report information about adverse events. Paradoxically, systematic elicitation and collection of information often is sought and encouraged for efficacy evaluations but avoided and discouraged for safety evaluations.
Standards of data analysis for safety evaluations often are lower than standards for efficacy evaluations. For example, one or a few efficacy variables often are analyzed by inferential statistical analyses while multitudes of safety variables are partially analyzed and presented for descriptive purposes only. In addition, limitations in the methods for analyzing safety data often limit procedures for eliciting data about safety variables. A primary reason systematic elicitation often is discouraged during evaluations of safety is that the data are analyzed by examining event rates. Systematic elicitation tends to produce higher event rates than spontaneous report. High event rates in conventional safety evaluations tend to make treatments look bad.
The fourth reason why separation of safety and efficacy evaluations appears to be a problem is that conventional procedures for combining generalized conclusions about efficacy with generalized conclusions about safety are quite limited. For this reason, the overall benefit and harm of treatments often is evaluated by subjective impressions (Section 1.2.1.2.3).
1.2.1.2.1.3. The Need to Use Early Responses to Predict Later Responses
There often is need in treatment evaluations to use the limited information that is currently available to predict longer-term responses. Such predictions can be used both during clinical practice and clinical trials to help minimize harm and maximize benefit. Here are three examples of this need.
Benefits with respect to primary target symptoms for some treatments are delayed substantially. In such cases, benefit with respect to some other measures may occur earlier and predict longer-term improvement. Many treatments, such as those used to lower blood pressure and change the concentrations of lipid fractions including cholesterol components are administered primarily to reduce longer-term risks of events such as major cardiovascular events or death. Particular short-term changes in liver enzymes may or may not predict longer-term events such as liver failure.
Quantitative monitoring procedures that can be applied sequentially are needed to predict the benefit and harm of treatments, procedures that are less reliant on subjective impressions and human vigilance.
1.2.1.2.1.4. The Classification Problem
Classifications of medical disorders are useful tools for matching individual patients with particular treatments in clinical practice and for targeting the development of new treatments. However, classifications such as the Diagnostic and Statistical Manual for Mental Disorders need to be used with caution while developing treatments for many heterogeneous chronic disorders. A potential problem will be briefly illustrated in the context of clinical depression and anxiety.
Clinical depression and anxiety are syndromes that involve many symptoms that often vary over time within patients. In addition, although the two diagnoses often are sufficiently distinct to be useful, there appear to be many patients with mixed varieties of anxiety and depression in which symptoms of the two diagnoses overlap. This state of affairs can complicate and hinder the development of new treatments such as drugs.
The classification problem can become evident in drug development because established treatments with particular profiles of benefit/harm across the spectrum of anxiety and depression symptoms tend to validate diagnostic conventions. This can impose a bias against regulatory agency approval of new drugs that have non-conventional profiles of benefit/harm across the same range of symptoms. This bias against new treatments can arise if new treatments with comparable or superior benefit/harm over or across a particular range of health measures are also required to demonstrate comparable or superior benefit/harm in accord with conventional diagnostic conventions. This problem can be illustrated with a simple hypothetical example.
Suppose symptoms A and B are part of the conventional requirements of an indication for treatment of depression and that symptoms C and D are part of the conventional requirements of an indication for treatment of anxiety. Suppose drug X was approved for the treatment of depression based on clinical trials that used a composite efficacy measure based on symptoms A and B. Similarly, drug Y was approved for the treatment of anxiety based on clinical trials that used a composite efficacy measure based on symptoms C and D. Now comes drug Z that is effective for the treatment of symptoms B and C. Assume that all four symptoms are equally important, that the beneficial effects of each drug on the symptoms it is effective in treating are equal in magnitude, that the three treatments are comparable in all other respects, and that there are many patients who need treatment for the combination of symptoms B and C. Further suppose that drug X has no effect on symptoms C and D, that drug Y has no effect on symptoms A and B, and that drug Z has no effect on symptoms A and D. Conventional drug evaluation procedures and guidelines could make it difficult to gain regulatory agency approval of drug Z because its profile of benefit/harm across symptoms is novel.
1.2.1.2.2. The Efficiency Problem in Clinical Trials
Another cluster of difficulties in the broader nexus will be called the efficiency problem. This problem involves the efficiency of using scarce resources to achieve clinical trial objectives such as obtaining statistical significance for treatments that have clinically significant effects as well as identifying subgroups of responders and indicators of differential response. Resources that often need to be used efficiently include patients, tests, money, and time.
The efficiency problem often involves tradeoffs between using resources for intensive versus extensive clinical trial designs. Conventional clinical trials rely primarily on what have been called extensive clinical trial designs. Designs tend to be extensive when they rely on relatively large numbers of patients and gain value from relatively small amounts of data from each patient. In contrast, intensive designs collect and gain value from more data, including larger numbers of repeated measurements, from each patient and tend to rely on smaller numbers of patients. Both types of design have important roles in clinical research.
One problem is that conventional extensive clinical trial designs are seldom an efficient way to achieve clinical trial objectives for treatments intended for the management and control of chronic disorders. Conventional extensive designs are particularly problematic when there is need to evaluate treatments for rare disorders and unusual patients. Patients with particular genomes and histories are unusual individuals. Treatments for rare disorders include orphan drugs.
The efficiency problem involves several components including unreliable measures of treatment and health, using independent variables as within patient variables, baselines, and missing data.
1.2.1.2.2.1. Problems that Derive from Unreliable Measures of Treatment and Health
A major factor contributing to the efficiency problem is that most measures of treatment and health have limited reliability. The limited reliability of health measures increases clinical trial sample size requirements when health measures are tested statistically. The limited reliability of treatment measures hinders exploratory analyses involving actual doses as well as the amounts of drug and drug metabolites in bodily fluids.
Despite careful and extensive development efforts, many health measures at all levels of health measurement hierarchies have limited reliability. One conventional way to increase the reliability of measures that are tested statistically is to test other measures whose values are computed from values of repeated measurements obtained with the relatively unreliable health measures. For example, some clinical trials test means of repeated health measurements, one mean for each individual. Another example is to test the slopes of regression lines through series of repeated health measurements obtained for each of the individuals from baseline through endpoint. One problem is that such procedures treat the variability of the repeated health measures as random error of measurement, which may not be true. For example, variability might be due to delayed responses to changes in treatments and other independent variables.
1.2.1.2.2.2. Problems Related to Limitations in Using Independent Variables as Within Patient Variables
Another major part of the efficiency problem is that conventional procedures for using repeated measurements to increase the reliability of health measures are not appropriate for evaluating treatment effects when independent variables such as dose are changing over time within patients. This, in turn, contributes to other problems such as the failure of conventional clinical trial designs and procedures to yield valid within patient measures of apparent benefit and harm.
Additional problems derive from limitations in using independent variables as within patient variables. For example, this limitation makes it difficult to evaluate dose-response relationships for individual patients by computational methods. It also results in failures to use potentially valuable information about dose when patients are gradually increased to higher doses because of safety concerns. Similarly, the limitation in using independent variables as within patient variables makes it difficult to develop scientifically rigorous clinical trial designs that allow optimization of doses for individual patients.
Limitations in using independent variables as within patient variables also make it difficult to conduct various exploratory analyses that have the potential to yield valuable information. Here are two examples. Very often, actual doses consumed by patients vary from the planned doses specified in clinical trial protocols. In addition, concentrations of drug and drug metabolites in bodily fluids often vary substantially even when different patients actually consume the same dose. Conventional clinical trial procedures make it difficult to reanalyze clinical trial results after substituting actual doses or concentrations of drug or of drug metabolites in bodily fluids for the planned doses specified in clinical trial protocols.
1.2.1.2.2.3. The Baseline Problem
Investigators using conventional clinical trial designs and procedures often attempt to identify or establish stable baselines. Values of dependent variables measured at baselines often become reference points for evaluating subsequent health effects of treatments. Here are two examples. Efficacy evaluations often involve multiple comparisons of a dependent variable measured for a patient at various time points during treatment with one value of the dependent variable obtained at baseline. Safety evaluations often involve analyses of treatment emergent signs and symptoms in which the signs and symptoms are considered to emerge from stable baseline health states.
One problem with designs and procedures that depend on stable baseline states is that stable baseline states often are elusive in a world of persistent and pervasive change. Attempts to find or establish stable baselines are especially problematic for certain disorders such as manic-depressive disorder, disorders for which manifestations are inherently variable over time.
Another problem derives from the fact that chronic disorders and treatments for chronic disorders generally are ongoing dynamic processes. Measurements at any particular times including baselines provide only static snapshots of information about dynamic functioning over portions of patient lifetimes. Similarly, as described in Section 1.2.1.2.6, measures of change between time points provide limited information about dynamic functioning.
Baselines tend to make certain measurements in a series more important than other measurements. It can be more difficult to obtain quality measures of apparent treatment effect if certain repeated measurements in a series for a particular patient are considered to be more important than other measurements in the series.
1.2.1.2.2.4. The Problem of Missing and Erroneous Data
Another consideration that tends to limit the use of intensive clinical trial designs, which use many repeated measurements, is that it may not be appropriate to apply statistical procedures such as analysis of variance to analyze repeated measures data when there are more than a few missing measurements. Missing data are common in clinical research.
Erroneous data also are not unusual in clinical research. The results of statistical analyses such as those based on means can be distorted severely by outliers. There is need for analytic procedures that are less apt to be distorted by outliers and more tolerant of missing data.
1.2.1.2.3. Problems Involving Soft Analyses of Clinical Trial Data
Treatment development programs typically yield vast amounts of rigorous scientific data, many high quality generalized conclusions based on many statistical tests, many subjective impressions based on soft analyses particularly of safety data, and many subjective impressions based on subjective experience. Regulatory agencies typically combine this material by complex subjective and social processes to arrive at generalized conclusions that may or may not support approval of particular treatments for marketing. Similarly, managed health care providers often combine this material by complex subjective and social processes to arrive at generalized conclusions that may or may not support approval of particular treatments for inclusion on formularies.
As a result of processes and procedures just described, some of the most important decisions about treatments are highly dependent on subjective impressions including subjective impressions resulting from soft analyses of clinical trial data. This dependence is a problem because subjective impressions have limited precision, repeatability, and reproducibility. Subjective impressions and soft analyses are defined in Section 1.2.1.1. Subjective impressions and soft analyses do not meet the criteria for high quality generalized conclusions as these criteria were defined in Section 1.2.
A major reason why important decisions about treatments are more dependent on soft analyses than these decisions need to be is that the statistical method is limited in its ability to investigate multidimensionality (Section 1.2.1). The generalized conclusions arrived at by conventional applications of the statistical method really are quite specific. For example, one statistical test may be applied to arrive at a generalized conclusion about the effects of a particular dose of treatment on a particular health variable. Problems often arise because it takes many statistical tests to analyze the effects of various doses on many health variables. In addition, problems arise because of a lack of computational procedures to combine the results of many statistical tests involving different variables to arrive at generalized conclusions about the overall benefit and harm of treatments for populations of patients, all variables considered.
In addition, conventional applications of the statistical method often limit extraction of value from data actually collected in clinical trials and often limit collection of data themselves. Typical group clinical trials yield more data than the amount of data that can be analyzed by conventional applications of the statistical method to arrive at high quality generalized conclusions. Still more potential data are not collected because the data can not be analyzed productively with conventional procedures. For example, most clinical trial investigators limit the collection of data from many repeated measurements of many health variables.
Here are some examples of how conventional clinical trial procedures foster reliance on subjective impressions during treatment evaluations. The first examples are for particular clinical trials. Subsequent examples are for sets of clinical trials for a particular treatment.
Conventional clinical trials, often in accord with regulatory agency guidelines for drug development, generally separate efficacy from safety evaluations (Section 1.2.1.2.1.2.5). Statistical testing often is reserved for efficacy evaluations. These conventions and guidelines increase reliance on subjective impressions in various ways. For example, it is not unusual for efficacy evaluations in particular trials to involve more than one statistical test at a particular level in a hierarchy of health measurement. Each of these tests may yield somewhat different results. Efficacy across multiple health measures often is evaluated by forming subjective impressions about the results of these multiple tests because of the lack of any widely accepted procedure that is defined in sufficient operational detail to be performed by computation.
Conventional clinical trial procedures and guidelines foster reliance on subjective impressions about the effects of treatment in particular trials in additional ways. The results of safety evaluations often are presented descriptively without statistical testing. Thus safety evaluations often are more reliant on subjective impressions than efficacy evaluations. Multiple subjective impressions about efficacy and safety then are combined to form more generalized subjective impressions about benefit and harm across all the efficacy and safety measures that were included in a particular trial.
Conventional clinical trial procedures also foster many clinical trials for particular treatments, multiple trials that are not true replications. The different trials may, for example, focus on different signs, symptoms, and other health measures. Subjective impressions about the overall effects of treatment from each of many trials often are combined to form still more generalized subjective impressions about the overall benefit and harm of particular treatments. All of these situations that involve forming more generalized subjective impressions from less generalized subjective impressions may further reduce the precision, repeatability, and reproducibility as well as the transparency of treatment evaluations.
1.2.1.2.4. Problems Related to Incompatibilities between Procedures for Rigorous Science and Quality Clinical Care
Conventional group clinical trial designs and procedures tend to be incompatible with procedures for optimizing care and outcomes for individual patients. As examples, trial patients may be randomized to groups that receive placebo only, sub-optimal doses, or potentially excessive doses. In addition, group clinical trial patients often are randomized to particular fixed dose groups. In contrast to the demands of conventional group clinical trial designs and procedures, doses often are adjusted in clinical practice in accord with the needs of particular patients. The primary reason why conventional group clinical trials use placebo only groups and fixed doses rather than doses optimized to meet the needs of individual patients is that the statistical method is not well suited to use independent variables as within patient variables (Section 2.3).
The incompatibility of conventional clinical trial and quality patient care procedures is widely acknowledged. It often is noted that group clinical trials are conducted primarily to gain knowledge for the benefit of patients other than those who participate in the trials.
The incompatibility of conventional clinical trial and patient care procedures creates often-avoidable conflicts between demands for scientific rigor in treatment evaluations and quality patient care. This incompatibility becomes evident in two important ways. The first and by far the most widely recognized problem is the ethical problem. For example, many people would question the ethics of assigning patients to placebo only groups when there are other viable options for achieving scientifically rigorous treatment evaluations. Concerns about ethical issues may deter some physicians from enrolling patients into group clinical trials and some patients from agreeing to participate in such trials. The second problem is that the incompatibility of procedures tends to limit the acquisition of cumulative scientific experience from quality patient care.
1.2.1.2.5. The Failure to Reveal Longitudinal Associations: An Example
Another problem is that conventional clinical trial procedures based on analyses of cross-sectional associations may fail to reveal important longitudinal associations between variables. This has been demonstrated with a two-part example using hypothetical data. This example involves concentrations of a hormone and values of a health variable that both always occur at some nonzero value for each patient. Suppose that each patient in two groups is measured or assessed at two points in time, baseline and endpoint. Also suppose that a treatment such as hormone supplementation doubles both the concentration of the hormone and the value of the health variable between baseline and endpoint for each patient in the treatment group. This doubling between the time points will be called the doubling effect of treatment. Also suppose that placebo has no effect on either hormone concentrations or values of the health variable for any patient in a placebo group that is otherwise identical to the treatment group.
Given the conditions of this hypothetical example, conventional clinical trial procedures would have little difficulty revealing a treatment effect by comparing the two groups. This comparison could involve either values of the health variable at endpoint or changes in the health variable from baseline to endpoint.
Assume that a secondary objective of the clinical trial in this hypothetical example is to examine any association that may exist between hormone concentrations and values of the health variable. Presumably if a treatment such as hormone supplementation has an effect on health, there should be an association between hormone concentrations and values of the health variable. Failure to reveal such an association could call into question trial results favorable to treatment. This example will continue by showing how detection of the association between hormone concentrations and values of the health variable may depend both on conditions at baseline and the method used for analyzing the data. In this example, the doubling effect of treatment means that there is an association between hormone concentrations and health variable values for each patient in the treatment group. This association is an example of a longitudinal association. Longitudinal associations may be present in the absence of cross-sectional associations.
The two parts of the example just introduced correspond to two extreme conditions and will focus on the treatment group only. For the first part of the example, assume that there is a straight-line relationship, indicated by a correlation coefficient with a value of 1, between hormone concentrations and health variable values across patients in the treatment group at baseline. After the doubling effect of treatment, straight-line relationships also will be evident across patients between hormone concentrations and health variable values at endpoint, between changes in concentrations and changes in health variable values, and between the mean concentrations and mean health variable values from the two measurements for each patient. The absolute value of the benefit/harm score (B/H score) for each patient is 1, the maximum value possible for this within patient measure when there are only two repeated measurements per patient. All of these analytic options provide evidence of an association between hormone concentrations and health variable values when the association is present both across and within patients.
For the second part of this example, assume that there is no association, indicated by a correlation coefficient with a value of 0, between hormone concentrations and health variable values across patients in the treatment group at baseline. After the doubling effect of treatment, no associations will be evident across patients between concentrations and health variable values at endpoint, between changes in concentrations and changes in health variable values, or between the mean concentrations and mean health variable values from the two measurements for each patient. In contrast, the absolute value of the B/H score for each patient still is 1.
Of all these across and within patient analytic options for the second part of this example, only the B/H score option provided evidence of the association between hormone concentrations and health variable values. This longitudinal association is present in this example because of what has been called the doubling effect of treatment. Analyzing longitudinal associations when there was no association between the same variables across patients revealed the association between the variables within patients. The second part of this example raises the intriguing possibility that certain important associations between variables that may exist within individual patients may not be revealed by conventional cross-sectional clinical trial data analysis procedures.
It is not clear how often conventional cross-sectional analyses fail to reveal important longitudinal associations between variables within patients. Quite often, correlation coefficients between variables that are part of internal control processes appear to be surprisingly weak when analyzed across patients. One possible explanation for this is that internal control processes tend to work at different values of the variables for different patients. If internal control processes within individuals can be likened to spoken communications among people, it is as if loud voices can compensate for insensitive hearing and sensitive hearing can compensate for weak voices. Compensation is one mechanism that might account for longitudinal associations that regulate in the absence of strong cross-sectional associations.
Treatments used to manage or control chronic disorders frequently involve internal control mechanisms. Chronic treatments for chronic disorders often may be considered to involve the use of exogenous agents to modify or restore natural internal control mechanisms. Failures of conventional clinical trial procedures to quantify, discover, analyze, and describe internal control mechanisms with analytic options that are sensitive to longitudinal associations that may be present in the absence of strong cross-sectional associations can hinder the development of treatments and the care of patients.
1.2.1.2.6. The Need to Investigate Dynamic Functioning Including Internal Control
Dynamic functioning is functioning in which both independent and dependent variables vary over time for an individual. Longitudinal associations indicate dynamic functioning.
Health, a multidimensional construct, is controlled by a dynamic interplay of internal and external agents. These agents are independent variables that actually affect health. Conventional clinical trial designs and procedures are limited in their ability to deal both with the multidimensionality and the dynamism of this interplay.
Most previous clinical trial sections, particularly those about the need for both detailed and comprehensive information from clinical trials, identified problems and needs related to multidimensionality. This section is about problems involving dynamism.
One reason why conventional clinical trial procedures are limited in their capacity to investigate dynamic functioning is that the conventional procedures do not provide high quality measures of dynamic functioning. Furthermore, conventional clinical trial procedures are limited in their capacity to measure and investigate change. Dynamic functioning involves changes.
Conventional clinical trial procedures often investigate change by computing differences between numbers obtained from repeated health measurements. For example, some clinical trials analyze differences between endpoint and baseline measurements. Differences between numbers from repeated measurements are of limited quality as measures of change or dynamic functioning for at least two reasons. The first reason is that two repeated measurements do not provide reliable measures of change or dynamic functioning when the measures have limited reliability.
The second reason why differences are of limited value for investigations involving change or dynamic functioning is that the number of differences increases rapidly with the number of repeated measurements. For example, the number of differences for one dependent variable increases rapidly as n(n-1)/2 where n equals the number of repeated measurements. Thus when there are 10 repeated measurements there are 45 differences in which the results of an earlier measurement are subtracted from a later measurement. The statistical method does not provide a means for dealing with large numbers of differences for individuals. Much information in data from clinical trials that collect more than two repeated measurements is underutilized because of such limitations.
High quality measures of dynamic functioning call for use of information from repeated measurements of both independent and dependent variables for an individual. The reason why conventional clinical trial designs and procedures are limited from going beyond the measurement of change to the measurement of dynamic functioning is that the statistical method is not well suited to use independent variables as within individual measures (Section 2.3). Independent variables need to vary and be measured together with dependent variables, preferably during applications of the experimental method, in order to quantify dynamic functioning and to explicate cause and effect relationships between and among the variables.
Dynamic functioning includes internal control. Internal control is dynamic functioning in which the independent variable is internal to the individual. For example, it is normal for a person's body to produce insulin which can help control glucose values. Internal control often is called regulatory control if it involves physiological mechanisms, self-control if it involves psychological mechanisms, or social control if it involves social mechanisms for groups.
Dynamic functioning also includes external control. External control is dynamic functioning in which the independent variable is external to the individual. Clinical trials of many treatments for the management of chronic disorders essentially are trials of external agents that may supplement, restore, enhance, or modify internal control mechanisms that involve health measures. For example, treating diabetic patients with exogenous insulin may help restore control of glucose values.
Many treatments for the management of chronic disorders work by affecting internal control mechanisms. As examples, some drugs for the treatment of adult onset diabetes work by sensitizing body tissues to the effects of insulin, normally an internal agent. Treatments for mental disorders often consist of exogenous agents considered to up-regulate or down-regulate components of disordered neurotransmitter systems. Knowledge about internal control mechanisms can contribute to rational drug development and use.
A fundamental problem of conventional clinical trial designs and procedures for treatments intended to manage chronic disorders is that the trials proceed without measuring longitudinal associations that quantify dynamic functioning. Measures of dynamic functioning that quantify external control are needed to evaluate the benefit and harm of treatments. Measures of dynamic functioning that quantify internal control often are needed to investigate how treatments work and how bodies function.
1.2.2. Citations
The present invention is a major improvement on work that the author has published. The article by C. A. Bagne and R. L. Lewis entitled "Evaluating the Effects of Drugs on Behavior and Quality of Life: An Alternative Strategy for Clinical Trials", JOURNAL OF CONSULTING AND CLINICAL PSYCHOLOGY, 1992, Vol. 60. No. 2, 225-239 describes a method for quantifying the benefit/harm of treatment, as benefit/harm becomes evident in the form of longitudinal associations between repeated measurements of one treatment variable and measures of health. B/H scores are longitudinal association scores (LASs) that may have had their signs may have been changed so that all positive LASs indicate benefit and all negative LASs indicate harm. Benefit/harm in this publication is quantified only as functions of level of an independent variable, levels of dependent variables, and delay.
The author of this document was the author or co-author of several abstracts that presented early versions of some aspects of the current invention. These abstracts are (1) R. C. Berchou and C. A. Bagne, "Quantifying treatment effects in the elderly", DRUG INTELLIGENCE AND CLINICAL PHARMACY, 1986, Vol. 20, 460 (2) C. A. Bagne, "Group comparison and follow-up evidence: Two sources of information about treatment effects", JOURNAL OF CLINICAL RESEARCH AND DRUG DEVELOPMENT, 1988, Vol. 2, 200 (3) C. A. Bagne, "Clinical Pharmacoepidemiology and Benefit Scoring", CLINICAL RESEARCH AND PHARMACOEPIDEMIOLOGY, Vol. 4, 115-116 (4) C. A. Bagne and D. F. Kraemer, "The use of benefit/harm scoring to evaluate longitudinal associations between treatment and quality of life: Application to clinical trials", DRUG INFORMATION JOURNAL, Vol. 27, 876-877 and (5) D. F. Kraemer and C. A, Bagne, "Monte Carlo simulation study of benefit/harm scoring in clinical trials that evaluate the effects of treatment on quality of life", DRUG INFORMATION JOURNAL, Vol. 27, 877. In addition, the author of this document has given presentations that have been abstracted in meeting programs. These abstracts are (1) C. A. Bagne, "Measurement of dysregulation", Society of Biological Psychiatry, May 6-10, 1987 (2) C. A. Bagne, "Outcome assessment can be improved by using measures of longitudinal association between treatment and health", Society for Clinical Trials, May 17-20, 1987, and (3) C. A. Bagne, "Evaluating overall drug utility without multiplicity: The case for benefit/harm scoring", Drug Information Association, March 29-31, 1992.
Several patents are potentially related to the present invention. U.S. Pat. No. 5,715,451 involves a method and system for constructing formulae for processing time-indexed medical values.
U.S. Pat. No. 5,742,811 involves a method and system for mining generalized sequential patterns from a large database of data sequences.
U.S. Pat. No. 5,251,126 presents an automated diabetes data interpretation method.
U.S. Pat. No. 5,672,154 presents a method and device for giving patients individualized medication advice that includes inductive data analyses for spotting relationships between various events and symptoms.
U.S. Pat. No. 5,640,549 presents an apparatus and method for determining the course of a patient's illness and response to treatment.
U.S. Pat. No. 5,262,943 presents a system that manages patient information and assessment information associated with those patients.
U.S. Pat. No. 5,544,281 appears to address the problem of predicting values of a time-series variable by comparing an emerging pattern with stored knowledge of previously observed patterns for the same variable. Prediction is based on an "unfolding" of a previously observed pattern for the same variable.
U.S. Pat. No. 5,563,983 predicts an output result using a learning system that involves a neural network. In addition, the prediction from this invention is based on the past history of the predicted variable. One problem is that it may be very difficult or impossible to precisely identify conditions within the data that account for the successful prediction. This makes if difficult to learn about the nature of things.
U.S. Pat. No. 5,412,769 involves the retrieval of time-series information, particularly non-numeric data. "IF-THEN" statements are used to make predictions.
U.S. Pat. No. 5,267,139 estimates parameters for "black box" systems whose interior dynamics are known to be linear, autonomous (time-invariant) and defined uniquely by parameters.
U.S. Pat. Nos. 5,504,569 and 5,694,129 address earthquake prediction and distance-velocity predicting.